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Volume 21, Issue 4
  • ISSN: 1567-2050
  • E-ISSN: 1875-5828

Abstract

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; the future of AD drug development.

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2024-04-01
2024-11-21

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References

  1. TatulianS.A. Challenges and hopes for Alzheimer’s disease.Drug Discov. Today20222741027104310.1016/j.drudis.2022.01.01635121174
     [Google Scholar]
  2. AdavS.S. SzeS.K. Insight of brain degenerative protein modifications in the pathology of neurodegeneration and dementia by proteomic profiling.Mol. Brain2016919210.1186/s13041‑016‑0272‑927809929
     [Google Scholar]
  3. PengC. TrojanowskiJ.Q. LeeV.M.Y. Protein transmission in neurodegenerative disease.Nat. Rev. Neurol.202016419921210.1038/s41582‑020‑0333‑732203399
     [Google Scholar]
  4. 2023 Alzheimer’s disease facts and figures.Alzheimers Dement.20231941598169510.1002/alz.1301636918389
     [Google Scholar]
  5. 2022 Alzheimer’s disease facts and figures.Alzheimers Dement.202218470078910.1002/alz.1263835289055
     [Google Scholar]
  6. YiannopoulouK.G. PapageorgiouS.G. Current and future treatments in Alzheimer disease: An update.J. Cent. Nerv. Syst. Dis.20201210.1177/117957352090739732165850
     [Google Scholar]
  7. LivingstonG. HuntleyJ. SommerladA. AmesD. BallardC. BanerjeeS. BrayneC. BurnsA. Cohen-MansfieldJ. CooperC. CostafredaS.G. DiasA. FoxN. GitlinL.N. HowardR. KalesH.C. KivimäkiM. LarsonE.B. OgunniyiA. OrgetaV. RitchieK. RockwoodK. SampsonE.L. SamusQ. SchneiderL.S. SelbækG. TeriL. MukadamN. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission.Lancet20203961024841344610.1016/S0140‑6736(20)30367‑632738937
     [Google Scholar]
  8. CummingsJ. LeeG. RitterA. SabbaghM. ZhongK. Alzheimer’s disease drug development pipeline: 2019.Alzheimers Dement. (N. Y.)20195127229310.1016/j.trci.2019.05.00831334330
     [Google Scholar]
  9. ShuklaR. SinghT.R. Virtual screening, pharmacokinetics, molecular dynamics and binding free energy analysis for small natural molecules against cyclin-dependent kinase 5 for Alzheimer’s disease.J. Biomol. Struct. Dyn.202038124826210.1080/07391102.2019.157194730688165
     [Google Scholar]
  10. MouchlisV.D. MelagrakiG. ZachariaL.C. AfantitisA. Computer-aided drug design of β-secretase, γ-secretase and anti-tau inhibitors for the discovery of novel Alzheimer’s therapeutics.Int. J. Mol. Sci.202021370310.3390/ijms2103070331973122
     [Google Scholar]
  11. JabirN.R. RehmanM.T. AlsolamiK. ShakilS. ZughaibiT.A. AlserihiR.F. KhanM.S. AlAjmiM.F. TabrezS. Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: In pursuit of Alzheimer’s treatment.Ann. Med.20215312332234410.1080/07853890.2021.200912434889159
     [Google Scholar]
  12. GriffithsJ. GrantS.G.N. Synapse pathology in Alzheimer’s disease.Semin. Cell Dev. Biol.2023139132310.1016/j.semcdb.2022.05.02835690535
     [Google Scholar]
  13. AshrafianH. ZadehE.H. KhanR.H. Review on Alzheimer’s disease: Inhibition of amyloid beta and tau tangle formation.Int. J. Biol. Macromol.202116738239410.1016/j.ijbiomac.2020.11.19233278431
     [Google Scholar]
  14. Trejo-LopezJ.A. YachnisA.T. ProkopS. Neuropathology of Alzheimer’s disease.Neurotherapeutics202219117318510.1007/s13311‑021‑01146‑y34729690
     [Google Scholar]
  15. ThalD.R. ToméS.O. The central role of tau in Alzheimer’s disease: From neurofibrillary tangle maturation to the induction of cell death.Brain Res. Bull.202219020421710.1016/j.brainresbull.2022.10.00636244581
     [Google Scholar]
  16. LeeW.J. BrownJ.A. KimH.R. La JoieR. ChoH. LyooC.H. RabinoviciG.D. SeongJ.K. SeeleyW.W. Regional Aβ- tau interactions promote onset and acceleration of Alzheimer's disease tau spreading.Neuron20221101219321943.e510.1016/j.neuron.2022.03.034
     [Google Scholar]
  17. KarranE. De StrooperB. The amyloid hypothesis in Alzheimer disease: New insights from new therapeutics.Nat. Rev. Drug Discov.202221430631810.1038/s41573‑022‑00391‑w35177833
     [Google Scholar]
  18. SongC. ShiJ. ZhangP. ZhangY. XuJ. ZhaoL. ZhangR. WangH. ChenH. Immunotherapy for Alzheimer’s disease: Targeting β-amyloid and beyond.Transl. Neurodegener.20221111810.1186/s40035‑022‑00292‑335300725
     [Google Scholar]
  19. WalkerL.C. Aβ plaques.Free Neuropathol.20201131302510.17879/freeneuropathology‑2020‑3025
     [Google Scholar]
  20. ZhangY. ChenH. LiR. SterlingK. SongW. Amyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future.Signal Transduct. Target. Ther.20238124810.1038/s41392‑023‑01484‑737386015
     [Google Scholar]
  21. BellenguezC. Grenier-BoleyB. LambertJ.C. Genetics of Alzheimer’s disease: Where we are, and where we are going.Curr. Opin. Neurobiol.202061404810.1016/j.conb.2019.11.02431863938
     [Google Scholar]
  22. BordoneM.P. SalmanM.M. TitusH.E. AminiE. AndersenJ.V. ChakrabortiB. DiubaA.V. DubouskayaT.G. EhrkeE. Espindola de FreitasA. Braga de FreitasG. GonçalvesR.A. GuptaD. GuptaR. HaS.R. HemmingI.A. JaggarM. JakobsenE. KumariP. LakkappaN. MarshA.P.L. MitlöhnerJ. OgawaY. PaidiR.K. RibeiroF.C. SalamianA. SaleemS. SharmaS. SilvaJ.M. SinghS. SulakhiyaK. TeferaT.W. VafadariB. YadavA. YamazakiR. SeidenbecherC.I. The energetic brain – A review from students to students.J. Neurochem.2019151213916510.1111/jnc.1482931318452
     [Google Scholar]
  23. SinskyJ. PichlerovaK. HanesJ. Tau protein interaction partners and their roles in Alzheimer’s disease and other tauopathies.Int. J. Mol. Sci.20212217920710.3390/ijms2217920734502116
     [Google Scholar]
  24. VenkatramaniA. PandaD. Regulation of neuronal microtubule dynamics by tau: Implications for tauopathies.Int. J. Biol. Macromol.201913347348310.1016/j.ijbiomac.2019.04.12031004638
     [Google Scholar]
  25. AisenP.S. Jimenez-MaggioraG.A. RafiiM.S. WalterS. RamanR. Early-stage Alzheimer disease: Getting trial-ready.Nat. Rev. Neurol.202218738939910.1038/s41582‑022‑00645‑635379951
     [Google Scholar]
  26. Ovejero-BenitoM.C. OchoaD. Enrique-BeneditoT. del Peso- CasadoM. ZubiaurP. NavaresM. RománM. Abad-SantosF. Pharmacogenetics of donepezil and memantine in healthy subjects.J. Pers. Med.202212578810.3390/jpm1205078835629210
     [Google Scholar]
  27. LiX. JiaY. LiJ. ZhangP. LiT. LuL. YaoH. LiuJ. ZhuZ. XuJ. Novel and potent acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease from natural (±)-7,8-dihydroxy-3-methyl-isochroman-4-one.Molecules20222710309010.3390/molecules2710309035630563
     [Google Scholar]
  28. CalhounA. KingC. KhouryR. GrossbergG.T. An evaluation of memantine ER + donepezil for the treatment of Alzheimer’s disease.Expert Opin. Pharmacother.201819151711171710.1080/14656566.2018.151902230244611
     [Google Scholar]
  29. ShiM. ChuF. ZhuF. ZhuJ. Impact of anti-amyloid-β monoclonal antibodies on the pathology and clinical profile of Alzheimer’s disease: A focus on aducanumab and lecanemab.Front. Aging Neurosci.20221487051710.3389/fnagi.2022.87051735493943
     [Google Scholar]
  30. SimsJ.R. ZimmerJ.A. EvansC.D. LuM. ArdayfioP. SparksJ. WesselsA.M. ShcherbininS. WangH. Monkul NeryE.S. CollinsE.C. SolomonP. SallowayS. ApostolovaL.G. HanssonO. RitchieC. BrooksD.A. MintunM. SkovronskyD.M. AbreuR. AgarwalP. AggarwalP. AgroninM. AllenA. AltamiranoD. AlvaG. AndersenJ. AndersonA. AndersonD. ArnoldJ. AsadaT. AsoY. AtitV. AyalaR. BadruddojaM. Badzio-jagielloH. BajacekM. BartonD. BearD. BenjaminS. BergeronR. BhatiaP. BlackS. BlockA. BolouriM. BondW. BouthillierJ. BrangmanS. BrewB. BrisbinS. BriskenT. BrodtmannA. BrodyM. BroschJ. BrownC. BrownstoneP. BukowczanS. BurnsJ. CabreraA. CapoteH. CarrascoA. Cevallos YepezJ. ChavezE. ChertkowH. Chyrchel-paszkiewiczU. CiabarraA. ClemmonsE. CohenD. CohenR. CohenI. ConchaM. CostellB. CrimminsD. Cruz-paganY. CueliA. CupeloR. CzarneckiM. DarbyD. DautzenbergP. De DeynP. De La GandaraJ. DeckK. DibenedettoD. DibuonoM. DinnersteinE. DiricanA. DixitS. DobryniewskiJ. DrakeR. DrysdaleP. DuaraR. DuffyJ. EllenbogenA. FaradjiV. FeinbergM. FeldmanR. FishmanS. FlitmanS. ForchettiC. FragaI. FrankA. FrishbergB. FujigasakiH. FukaseH. FumeroI. FurihataK. GallowayC. GandhiR. GeorgeK. GermainM. GitelmanD. GoetschN. GoldfarbD. GoldsteinM. GoldstickL. Gonzalez RojasY. GoodmanI. GreeleyD. GriffinC. GrigsbyE. GroszD. HafnerK. HartD. HeneinS. HerskowitzB. HigashiS. HigashiY. HoG. HodgsonJ. HohenbergM. HollenbeckL. HolubR. HoriT. HortJ. IlkowskiJ. IngramK.J. IsaacM. IshikawaM. JanuL. JohnstonM. JulioW. JustizW. KagaT. KakigiT. KalaferM. KamijoM. KaplanJ. KarathanosM. KatayamaS. KaulS. KeeganA. KerwinD. KhanU. KhanA. KimuraN. KirkG. KlodowskaG. KowaH. KutzC. KwentusJ. LaiR. LallA. LawrenceM. LeeE. LeonR. LinkerG. LisewskiP. LissJ. LiuC. LoskS. LukaszykE. LynchJ. MacfarlaneS. MacsweeneyJ. ManneringN. MarkovicO. MarksD. MasdeuJ. MatsuiY. MatsuishiK. McallisterP. McconneheyB. McelveenA. McgillL. MeccaA. MegaM. MensahJ. MickielewiczA. MinaeianA. MocherlaB. MurphyC. MurphyP. NagashimaH. NairA. NairM. NardandreaJ. NashM. NasreddineZ. NishidaY. NortonJ. NunezL. OchiaiJ. OhkuboT. OkamuraY. OkorieE. OliveraE. O’mahonyJ. OmidvarO. Ortiz-CruzD. OsowaA. PapkaM. ParkerA. PatelP. PatelA. PatelM. PatryC. PeckhamE. PfefferM. PietrasA. PlopperM. PorsteinssonA. RobitailleP.R. PrinsN. PuenteO. RatajczakM. RheeM. RitterA. RodriguezR. AblesR.L. RojasJ. RossJ. RoyerP. RubinJ. RussellD. RutgersS.M. RutrickS. SadowskiM. SafirsteinB. SagisakaT. ScharreD. SchneiderL. SchreiberC. SchriftM. SchulzP. SchwartzH. SchwartzbardJ. ScottJ. SelemL. SethiP. ShaS. SharlinK. SharmaS. ShiovitzT. ShiwachR. SladekM. SloanB. SmithA. SolomonP. SorialE. SosaE. StedmanM. SteenS. SteinL. StolyarA. StoukidesJ. SudohS. SuttonJ. SyedJ. SzigetiK. TachibanaH. TakahashiY. TatenoA. TaylorJ.D. TaylorK. TcheremissineO. ThebaudA. TheinS. ThurmanL. ToenjesS. TojiH. TomaM. TranD. TruebaP. TsujimotoM. TurnerR. UchiyamaA. UssorowskaD. VaishnaviS. ValorE. VandersluisJ. VasquezA. VelezJ. VergheseC. Vodickova-borzovaK. WatsonD. WeidmanD. WeismanD. WhiteA. WillinghamK. WinkelI. WinnerP. WinstonJ. WolffA. YagiH. YamamotoH. YathirajS. YoshiyamaY. ZbochM. Donanemab in Early Symptomatic Alzheimer Disease.JAMA2023330651252710.1001/jama.2023.1323937459141
     [Google Scholar]
  31. Pardo-MorenoT. González-AcedoA. Rivas-DomínguezA. García-MoralesV. García-CozarF.J. Ramos-RodríguezJ.J. Melguizo-RodríguezL. Therapeutic approach to Alzheimer’s disease: Current treatments and new perspectives.Pharmaceutics2022146111710.3390/pharmaceutics1406111735745693
     [Google Scholar]
  32. BatemanR.J. CummingsJ. SchobelS. SallowayS. VellasB. BoadaM. BlackS.E. BlennowK. FontouraP. KleinG. AssunçãoS.S. SmithJ. DoodyR.S. Gantenerumab: An anti-amyloid monoclonal antibody with potential disease-modifying effects in early Alzheimer’s disease.Alzheimers Res. Ther.202214117810.1186/s13195‑022‑01110‑836447240
     [Google Scholar]
  33. VukicevicM. FioriniE. SiegertS. CarpinteroR. Rincon-RestrepoM. Lopez-DeberP. PiotN. AyerM. RenteroI. BabolinC. Bravo-VeyratS. GiriensV. MoriciC. BeuzelinM. GesbertA. RivotS. DeprettiS. DonatiP. StrefferJ. PfeiferA. Kosco-VilboisM.H. An amyloid beta vaccine that safely drives immunity to a key pathological species in Alzheimer’s disease: Pyroglutamate amyloid beta.Brain Commun.202241fcac02210.1093/braincomms/fcac02235479516
     [Google Scholar]
  34. DuboisB. López-ArrietaJ. LipschitzS. DoskasT. SpiruL. MorozS. VengerO. VermerschP. MoussyA. MansfieldC.D. HermineO. TsolakiM. Masitinib for mild-to-moderate Alzheimer’s disease: Results from a randomized, placebo-controlled, phase 3, clinical trial.Alzheimers Res. Ther.20231513910.1186/s13195‑023‑01169‑x36849969
     [Google Scholar]
  35. HuangL.K. KuanY.C. LinH.W. HuC.J. Clinical trials of new drugs for Alzheimer disease: A 2020–2023 update.J. Biomed. Sci.20233018310.1186/s12929‑023‑00976‑637784171
     [Google Scholar]
  36. VemulaD. JayasuryaP. SushmithaV. KumarY.N. BhandariV. CADD, AI and ML in drug discovery: A comprehensive review.Eur. J. Pharm. Sci.202318110632410.1016/j.ejps.2022.10632436347444
     [Google Scholar]
  37. BaigM.H. AhmadK. RabbaniG. DanishuddinM. ChoiI. Computer aided drug design and its application to the development of potential drugs for neurodegenerative disorders.Curr. Neuropharmacol.201816674074810.2174/1570159X1566617101616351029046156
     [Google Scholar]
  38. WangB. DaiP. DingD. Del RosarioA. GrantR.A. PenteluteB.L. LaubM.T. Affinity-based capture and identification of protein effectors of the growth regulator ppGpp.Nat. Chem. Biol.201915214115010.1038/s41589‑018‑0183‑430559427
     [Google Scholar]
  39. ChanH.C.S. LiY. DahounT. VogelH. YuanS. New binding sites, new opportunities for GPCR drug discovery.Trends Biochem. Sci.201944431233010.1016/j.tibs.2018.11.01130612897
     [Google Scholar]
  40. KimE.Y. ImJ.H. HanJ. ChoW.J. Structure-based design and synthesis of sulfonylureas as novel NLRP3 inhibitors for Alzheimer’s disease.Bioorg. Med. Chem. Lett.20249912962210.1016/j.bmcl.2024.12962238244940
     [Google Scholar]
  41. JumperJ. EvansR. PritzelA. GreenT. FigurnovM. RonnebergerO. TunyasuvunakoolK. BatesR. ŽídekA. PotapenkoA. BridglandA. MeyerC. KohlS.A.A. BallardA.J. CowieA. Romera-ParedesB. NikolovS. JainR. AdlerJ. BackT. PetersenS. ReimanD. ClancyE. ZielinskiM. SteineggerM. PacholskaM. BerghammerT. BodensteinS. SilverD. VinyalsO. SeniorA.W. KavukcuogluK. KohliP. HassabisD. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑234265844
     [Google Scholar]
  42. Ragonis-BacharP. AxelG. BlauS. Ben-TalN. KolodnyR. LandauM. What can AlphaFold do for antimicrobial amyloids?Proteins202492226528110.1002/prot.2661837855235
     [Google Scholar]
  43. LlanesL.C. KuehleweinI. FrançaI.V. da SilvaL.V. da Cruz JuniorJ.W. Anticholinesterase agents for Alzheimer’s disease treatment: An updated overview.Curr. Med. Chem.202330670172410.2174/092986732966622080311341135927804
     [Google Scholar]
  44. LvM.T. WangH.C. MengX.W. ShiY.T. ZhangY.M. ShanL.L. ShiR.L. NiT.J. DuanY.C. YangZ.J. ZhangW. In silico and in vitro analyses of a novel FoxO1 agonist reducing Aβ levels via downregulation of BACE1.CNS Neurosci. Ther.2024303e1414010.1111/cns.1414036892036
     [Google Scholar]
  45. MakarasenA. KunoM. PatninS. ReukngamN. KhlaychanP. DeeyoheS. IntachoteP. SaimaneeB. SengsaiS. BoonsriP. ChaivisuthangkuraA. SirithanaW. TechasakulS. Molecular docking studies and synthesis of amino-oxy-diarylquinoline derivatives as potent non-nucleoside HIV-1 reverse transcriptase inhibitors.Drug Res. (Stuttg.)2019691267168210.1055/a‑0968‑115031698495
     [Google Scholar]
  46. VilarS. Sobarzo-SánchezE. UriarteE. In silico prediction of P-glycoprotein binding: Insights from molecular docking studies.Curr. Med. Chem.201926101746176010.2174/092986732566617112912192429189117
     [Google Scholar]
  47. YeW. WangW. JiangC. YuQ. ChenH. Molecular dynamics simulations of amyloid fibrils: An in silico approach.Acta Biochim. Biophys. Sin. (Shanghai)201345650350810.1093/abbs/gmt02623532062
     [Google Scholar]
  48. NunesR.R. FonsecaA.L. PintoA.C.S. MaiaE.H.B. SilvaA.M. VarottiF.P. TarantoA.G. Brazilian malaria molecular targets (BraMMT): Selected receptors for virtual high-throughput screening experiments.Mem. Inst. Oswaldo Cruz2019114e18046510.1590/0074‑0276018046530810604
     [Google Scholar]
  49. MuratovE.N. BajorathJ. SheridanR.P. TetkoI.V. FilimonovD. PoroikovV. OpreaT.I. BaskinI.I. VarnekA. RoitbergA. IsayevO. CurtaloloS. FourchesD. CohenY. Aspuru-GuzikA. WinklerD.A. AgrafiotisD. CherkasovA. TropshaA. QSAR without borders.Chem. Soc. Rev.202049113525356410.1039/D0CS00098A32356548
     [Google Scholar]
  50. KumarA. NandiS. SaxenaA.K. Antidepressant drug design on TCAs and phenoxyphenylpropylamines utilizing QSAR and pharmacophore modeling.Comb. Chem. High Throughput Screen.202225345146110.2174/138620732366620090110422232875980
     [Google Scholar]
  51. GiordanoD. BiancanielloC. ArgenioM.A. FacchianoA. Drug design by pharmacophore and virtual screening approach.Pharmaceuticals (Basel)202215564610.3390/ph1505064635631472
     [Google Scholar]
  52. GuptaR. SrivastavaD. SahuM. TiwariS. AmbastaR.K. KumarP. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery.Mol. Divers.20212531315136010.1007/s11030‑021‑10217‑333844136
     [Google Scholar]
  53. FangJ. ZhangP. WangQ. ChiangC.W. ZhouY. HouY. XuJ. ChenR. ZhangB. LewisS.J. LeverenzJ.B. PieperA.A. LiB. LiL. CummingsJ. ChengF. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease.Alzheimers Res. Ther.2022141710.1186/s13195‑021‑00951‑z35012639
     [Google Scholar]
  54. WinchesterL.M. HarshfieldE.L. ShiL. BadhwarA. KhleifatA.A. ClarkeN. DehsarviA. LengyelI. LouridaI. MadanC.R. MarziS.J. ProitsiP. RajkumarA.P. RittmanT. SilajdžićE. TamburinS. RansonJ.M. LlewellynD.J. Artificial intelligence for biomarker discovery in Alzheimer’s disease and dementia.Alzheimers Dement.202319125860587110.1002/alz.1339037654029
     [Google Scholar]
  55. PaulD. SanapG. ShenoyS. KalyaneD. KaliaK. TekadeR.K. Artificial intelligence in drug discovery and development.Drug Discov. Today2021261809310.1016/j.drudis.2020.10.01033099022
     [Google Scholar]
  56. SubramanianM. WojtusciszynA. FavreL. BoughorbelS. ShanJ. LetaiefK.B. PitteloudN. ChouchaneL. Precision medicine in the era of artificial intelligence: Implications in chronic disease management.J. Transl. Med.202018147210.1186/s12967‑020‑02658‑533298113
     [Google Scholar]
  57. VatanseverS. SchlessingerA. WackerD. KaniskanH.Ü. JinJ. ZhouM.M. ZhangB. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Med. Res. Rev.20214131427147310.1002/med.2176433295676
     [Google Scholar]
  58. RenF. AliperA. ChenJ. ZhaoH. RaoS. KuppeC. OzerovI.V. ZhangM. WitteK. KruseC. AladinskiyV. IvanenkovY. PolykovskiyD. FuY. BabinE. QiaoJ. LiangX. MouZ. WangH. PunF.W. AyusoP.T. VeviorskiyA. SongD. LiuS. ZhangB. NaumovV. DingX. KukharenkoA. IzumchenkoE. ZhavoronkovA. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.Nat. Biotechnol.2024202402143-010.1038/s41587‑024‑02143‑038459338
     [Google Scholar]
  59. PatelL. ShuklaT. HuangX. UsseryD.W. WangS. Machine Learning Methods in Drug Discovery.Molecules20202522527710.3390/molecules2522527733198233
     [Google Scholar]
  60. ZhavoronkovA. IvanenkovY.A. AliperA. VeselovM.S. AladinskiyV.A. AladinskayaA.V. TerentievV.A. PolykovskiyD.A. KuznetsovM.D. AsadulaevA. VolkovY. ZholusA. ShayakhmetovR.R. ZhebrakA. MinaevaL.I. ZagribelnyyB.A. LeeL.H. SollR. MadgeD. XingL. GuoT. Aspuru-GuzikA. Deep learning enables rapid identification of potent DDR1 kinase inhibitors.Nat. Biotechnol.20193791038104010.1038/s41587‑019‑0224‑x31477924
     [Google Scholar]
  61. AmarrehI. MeyerandM.E. StafstromC. HermannB.P. BirnR.M. Individual classification of children with epilepsy using support vector machine with multiple indices of diffusion tensor imaging.Neuroimage Clin.2014475776410.1016/j.nicl.2014.02.00624936426
     [Google Scholar]
  62. ShiC. DongF. ZhaoG. ZhuN. LaoX. ZhengH. Applications of machine-learning methods for the discovery of NDM-1 inhibitors.Chem. Biol. Drug Des.20209651232124310.1111/cbdd.1370832418370
     [Google Scholar]
  63. ZoffmannS. VercruysseM. BenmansourF. MaunzA. WolfL. Blum MartiR. HeckelT. DingH. TruongH.H. PrummerM. SchmuckiR. MasonC.S. BradleyK. JacobA.I. LernerC. Araujo del RosarioA. BurcinM. AmreinK.E. PrunottoM. Machine learning-powered antibiotics phenotypic drug discovery.Sci. Rep.201991501310.1038/s41598‑019‑39387‑930899034
     [Google Scholar]
  64. KashyapK. SiddiqiM.I. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.Mol. Divers.20212531517153910.1007/s11030‑021‑10274‑834282519
     [Google Scholar]
  65. HuY. ZhouG. ZhangC. ZhangM. ChenQ. ZhengL. NiuB. Identify compounds’ target against Alzheimer’s disease based on in silico approach.Curr. Alzheimer Res.201916319320810.2174/156720501666619010315485530605059
     [Google Scholar]
  66. JamalS. GroverA. GroverS. Machine learning from molecular dynamics trajectories to predict caspase-8 inhibitors against Alzheimer’s disease.Front. Pharmacol.20191078010.3389/fphar.2019.0078031354494
     [Google Scholar]
  67. XieC. ZhuangX.X. NiuZ. AiR. LautrupS. ZhengS. JiangY. HanR. GuptaT.S. CaoS. Lagartos-DonateM.J. CaiC.Z. XieL.M. CaponioD. WangW.W. Schmauck-MedinaT. ZhangJ. WangH. LouG. XiaoX. ZhengW. PalikarasK. YangG. CaldwellK.A. CaldwellG.A. ShenH.M. NilsenH. LuJ.H. FangE.F. Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow.Nat. Biomed. Eng.202261769310.1038/s41551‑021‑00819‑534992270
     [Google Scholar]
  68. AlokamR. SinghalS. SrivathsavG.S. GarigipatiS. PuppalaS. SriramD. PerumalY. Design of dual inhibitors of ROCK-I and NOX2 as potential leads for the treatment of neuroinflammation associated with various neurological diseases including autism spectrum disorder.Mol. Biosyst.201511260761710.1039/C4MB00570H25465055
     [Google Scholar]
  69. SimpsonD.S.A. OliverP.L. ROS generation in microglia: Understanding oxidative stress and inflammation in neurodegenerative disease.Antioxidants20209874310.3390/antiox908074332823544
     [Google Scholar]
  70. KhezriM.R. Ghasemnejad-BerenjiM. The role of caspases in Alzheimer’s disease: Pathophysiology implications and pharmacologic modulation.J. Alzheimers Dis.2023911719010.3233/JAD‑22087336442198
     [Google Scholar]
  71. AlkanliS.S. AlkanliN. AyA. AlbenizI. CRISPR/Cas9 mediated therapeutic approach in Huntington’s disease.Mol. Neurobiol.20236031486149810.1007/s12035‑022‑03150‑536482283
     [Google Scholar]
  72. ImbrianiP. TassoneA. MeringoloM. PonterioG. MadeoG. PisaniA. BonsiP. MartellaG. Loss of non-apoptotic role of caspase-3 in the PINK1 mouse model of Parkinson’s disease.Int. J. Mol. Sci.20192014340710.3390/ijms2014340731336695
     [Google Scholar]
  73. BrahadeeswaranS. SivagurunathanN. CalivarathanL. Inflammasome signaling in the aging brain and age-related neurodegenerative diseases.Mol. Neurobiol.20225942288230410.1007/s12035‑021‑02683‑535066762
     [Google Scholar]
  74. CancelaS. CancliniL. Mourglia-EttlinG. HernándezP. MerlinoA. Neuroprotective effects of novel nitrones: In vitro and in silico studies.Eur. J. Pharmacol.202087117292610.1016/j.ejphar.2020.17292631958456
     [Google Scholar]
  75. XingY. LiA. YangY. LiX. ZhangL. GuoH. The regulation of FOXO1 and its role in disease progression.Life Sci.201819312413110.1016/j.lfs.2017.11.03029158051
     [Google Scholar]
  76. ZhangW. BaiS. YangJ. ZhangY. LiuY. NieJ. MengD. ShiR. YaoZ. WangM. WangH. LiC. FoxO1 overexpression reduces Aβ production and tau phosphorylation in vitro.Neurosci. Lett.202073813532210.1016/j.neulet.2020.13532232860886
     [Google Scholar]
  77. ZhangW. BaiS.S. ZhangQ. ShiR.L. WangH.C. LiuY.C. NiT.J. WuY. YaoZ.Y. SunY. WangM.Y. Physalin B reduces Aβ secretion through down-regulation of BACE1 expression by activating FoxO1 and inhibiting STAT3 phosphorylation.Chin. J. Nat. Med.2021191073274010.1016/S1875‑5364(21)60090‑034688463
     [Google Scholar]
  78. CooperN. GhanimaW. HillQ.A. NicolsonP.L.R. MarkovtsovV. KesslerC. Recent advances in understanding spleen tyrosine kinase (SYK) in human biology and disease, with a focus on fostamatinib.Platelets2023341213175110.1080/09537104.2022.213175136331249
     [Google Scholar]
  79. EnnerfeltH. FrostE.L. ShapiroD.A. HollidayC. ZengelerK.E. VoithoferG. BolteA.C. LammertC.R. KulasJ.A. UllandT.K. LukensJ.R. SYK coordinates neuroprotective microglial responses in neurodegenerative disease.Cell20221852241354152.e2210.1016/j.cell.2022.09.03036257314
     [Google Scholar]
  80. Jiménez-LunaJ. GrisoniF. WeskampN. SchneiderG. Artificial intelligence in drug discovery: Recent advances and future perspectives.Expert Opin. Drug Discov.202116994995910.1080/17460441.2021.190956733779453
     [Google Scholar]
  81. DorahyG. ChenJ.Z. BalleT. Computer-aided drug design towards new psychotropic and neurological drugs.Molecules2023283132410.3390/molecules2803132436770990
     [Google Scholar]
  82. SongN. SunS. ChenK. WangY. WangH. MengJ. GuoM. ZhangX.D. ZhangR. Emerging nanotechnology for Alzheimer’s disease: From detection to treatment.J. Control. Release202336039241710.1016/j.jconrel.2023.07.00437414222
     [Google Scholar]
/content/journals/car/10.2174/0115672050329828240805074938
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Advances in Developing Small Molecule Drugs for Alzheimer's Disease
Curr Alzheimer Res 21, 221 (2024); https://doi.org/10.2174/0115672050329828240805074938
/content/journals/car/10.2174/0115672050329828240805074938
/content/journals/car/10.2174/0115672050329828240805074938
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  • Article Type:     Review Article
Keyword(s): Alzheime's disease; computer-aided drug design targets; forkhead box protein O1; pathogenesis; small molecule drugs; β-amyloid

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